It is very common during medical examinations for medical imaging systems (e.g., ultrasound imaging systems) to be used for the detection and diagnosis of abnormalities associated with anatomical structures (e.g., organs such as the heart). Many times, the images are evaluated by a medical expert (e.g., a physician or medical technician) who is trained to recognize characteristics in the images which could indicate an abnormality associated with the anatomical structure or a healthy anatomical structure.
Various types of approaches have been used to detect objects of interest (e.g., anatomical structures). Component-based object detectors can deal with large variations in pose and illumination, and are more robust under occlusions and heteroscedastic noise. For example, in echocardiogram analysis, local appearance of the same anatomical structure (e.g., the septum) is similar across patients, while the configuration or shape of the heart can be dramatically different due to, for example, viewing angles or disease conditions
Due to its availability, relative low cost, and noninvasiveness, cardiac ultrasound images are widely used for assessing cardiac functions. In particular, the analysis of ventricle motion is an efficient way to evaluate the degree of ischemia and infarction. Segmentation or detection of the endocardium wall is the first step towards quantification of elasticity and contractility of the left ventricle. Examples of some existing methods include pixel-based segmentation/clustering approaches (e.g., Color Kinesis), variants of optical flow, deformable templates and Markov random process/fields, and active contours/snakes. The methods are employed in 2-Dimensional, 3-Dimensional or 4-Dimensional (3D+time) space.
Segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task and solved through clustering and variational approaches. However, such strategies require a priori knowledge to be explicitly defined in the optimization criterion (e.g., “high-gradient border”, “smoothness” or “similar intensity or texture”). These approaches are limited by the validity of the underlying assumptions and cannot capture complex structure appearance.
Accurate localization of complex structures is important in many computer vision applications ranging from facial feature detection to segmentation of anatomical structures in medical images or volumes. Availability of large databases with expert annotation of the interest structures makes a learning approach more attractive than classical approaches of solving perceptual grouping tasks through clustering or variational formulations. This is especially important when the underlying image structure does not have clear border definition, show complex appearance with large amounts of noise, or when there is a relatively large variation between expert's own annotations.
Segmentation is one of the most important low level image processing methods and has been traditionally approached as a grouping task based on some homogeneity assumption. For example, clustering methods have been used to group regions based on color similarity or graph partitioning methods have been used to infer global regions with coherent brightness, color and texture. Alternatively, the segmentation problem can be cast in an optimization framework as the minimization of some energy function. Concepts such as “high-gradient border”, “smoothness” or “similar intensity or texture” are encoded as region or boundary functionality in the energy function and minimized through variational approaches.
However, as the complexity of targeted segmentation increases, it is more difficult to encode prior knowledge into the grouping task. Furthermore, it is computationally expensive to detect an anatomical structure in a 3D image directly. Learning has become more important for segmentation and there are methods that infer rules for the grouping process that are conditioned by the user input.
Automatic delineation of anatomical structures in 3D volumetric data is a challenging task due to the complexity of the object appearance as well as the quantity of information to be processed. While 3D volumetric data contains much richer information than 2D images, 3D volumetric data is still not widely used in clinical diagnosis mainly because quantitative analysis by humans is more time-consuming than analyzing 2D images. There is a need for a method which directly exploits expert annotation of the interest structure in large databases by formulating the segmentation as a learning problem.